Journal of Vascular Medicine & Surgery

Hospital Medicine, Northwell Health, New York, USA

Publications

• Review Article   
  Automated Lung Cancer Detection a Comparison amongst Physicians: A Literature Review
  Author(s): Kaviya Sathyakumar, Michael Munoz, Snehal Bansod, Jaikaran Singh, Jasmin Hundal and B Benson A. Babu*
  
  Introduction: Lung cancer is the number one cause of cancer-related deaths in the United States as well as worldwide. Radiologists and physicians experience heavy daily workloads thus are at high risk for burn-out. To alleviate this burden, this literature review compares the performance of four different AI models in lung nodule cancer detection, as well as their performance to physicians/radiologists. Methods: 648 articles were extracted from 2008 to 2019. 4/648 articles were selected. Inclusion criteria: 18-65 years old, CT chest scans, lung nodule, lung cancer, deep learning, ensemble and classic methods. Exclusion criteria: age greater than 65 years old, PET hybrid scans, CXR and genomics. Outcomes analysis: Sensitivity, specificity, accuracy, sensitivity-specificity ROC curve, Area under the curve (AUC). Data bases: Pub.. View more»
  

  DOI: 10.35248/2329-6925.21.9.407

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